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base_config.yaml
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/
base_config.yaml
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dataset:
data_path: '$ALFWORLD_DATA/json_2.1.1/train'
eval_id_data_path: '$ALFWORLD_DATA/json_2.1.1/valid_seen' # null/None to disable
eval_ood_data_path: '$ALFWORLD_DATA/json_2.1.1/valid_unseen' # null/None to disable
num_train_games: -1 # max training games (<=0 indicates full dataset)
num_eval_games: -1 # max evaluation games (<=0 indicates full dataset)
logic:
domain: '$ALFWORLD_DATA/logic/alfred.pddl' # PDDL domain file that defines the world dynamics
grammar: '$ALFWORLD_DATA/logic/alfred.twl2' # Grammar file that defines the text feedbacks
env:
type: 'AlfredTWEnv' # 'AlfredTWEnv' or 'AlfredThorEnv' or 'AlfredHybrid'
regen_game_files: False # check if game is solvable by expert and save to game.tw-pddl file
domain_randomization: False # shuffle Textworld print order and object id nums
task_types: [1, 2, 3, 4, 5, 6] # task-type ids: 1 - Pick & Place, 2 - Examine in Light, 3 - Clean & Place, 4 - Heat & Place, 5 - Cool & Place, 6 - Pick Two & Place
expert_timeout_steps: 150 # max steps before timeout for expert to solve the task
expert_type: "handcoded" # 'handcoded' or 'downward'. Note: the downward planner is very slow for real-time use
goal_desc_human_anns_prob: 0.0 # prob of using human-annotated goal language instead of templated goals (1.0 indicates all human annotations from ALFRED)
hybrid:
start_eps: 100000 # starting episode of hybrid training, tw-only training upto this point
thor_prob: 0.5 # prob of AlfredThorEnv during hybrid training
eval_mode: "tw" # 'tw' or 'thor' - env used for evaluation during hybrid training
thor:
screen_width: 300 # width of THOR window
screen_height: 300 # height of THOR window
smooth_nav: False # smooth rotations, looks, and translations during navigation (very slow)
save_frames_to_disk: False # save frame PNGs to disk (useful for making videos)
save_frames_path: './videos/' # path to save frame PNGs
controller:
type: 'oracle' # 'oracle' or 'oracle_astar' or 'mrcnn' or 'mrcnn_astar' (aka BUTLER)
debug: False
load_receps: True # load receptacle locations from precomputed dict (if available)
mask_rcnn:
pretrained_model_path: '$ALFWORLD_DATA/detectors/mrcnn.pth'
general:
random_seed: 42
use_cuda: True # disable this when running on machine without cuda
visdom: False # plot training/eval curves, run with visdom server
task: 'alfred'
training_method: 'dagger' # 'dqn' or 'dagger'
save_path: './training/' # path to save pytorch models
observation_pool_capacity: 3 # k-size queue, 0 indicates no observation
hide_init_receptacles: False # remove initial observation containing navigable receptacles
training:
batch_size: 10
max_episode: 50000
smoothing_eps: 0.1
optimizer:
learning_rate: 0.001
clip_grad_norm: 5
evaluate:
run_eval: True
batch_size: 10
env:
type: "AlfredTWEnv"
checkpoint:
report_frequency: 1000 # report every N episode
experiment_tag: 'test' # name of experiment
load_pretrained: False # during test, enable this so that the agent load your pretrained model
load_from_tag: 'not loading anything' # name of pre-trained model to load in save_path
model:
encoder_layers: 1
decoder_layers: 1
encoder_conv_num: 5
block_hidden_dim: 64
n_heads: 1
dropout: 0.1
block_dropout: 0.1
recurrent: True
rl:
action_space: "admissible" # 'admissible' (candidates from text engine) or 'generation' (seq2seq-style generation) or 'beam_search_choice' or 'exhaustive' (not working)
max_target_length: 20 # max token length for seq2seq generation
beam_width: 10 # 1 means greedy
generate_top_k: 3
training:
max_nb_steps_per_episode: 50 # terminate after this many steps
learn_start_from_this_episode: 0 # delay updates until this epsiode
target_net_update_frequency: 500 # sync target net with online net per this many epochs
replay:
accumulate_reward_from_final: True
count_reward_lambda: 0.0 # 0 to disable
novel_object_reward_lambda: 0.0 # 0 to disable
discount_gamma_game_reward: 0.9
discount_gamma_count_reward: 0.5
discount_gamma_novel_object_reward: 0.5
replay_memory_capacity: 500000 # adjust this depending on your RAM size
replay_memory_priority_fraction: 0.5
update_per_k_game_steps: 5
replay_batch_size: 64
multi_step: 3
replay_sample_history_length: 4
replay_sample_update_from: 2
epsilon_greedy:
noisy_net: False # if this is true, then epsilon greedy is disabled
epsilon_anneal_episodes: 1000 # -1 if not annealing
epsilon_anneal_from: 0.3
epsilon_anneal_to: 0.1
dagger:
action_space: "generation" # 'admissible' (candidates from text engine) or 'generation' (seq2seq-style generation) or 'exhaustive' (not working)
max_target_length: 20 # max token length for seq2seq generation
beam_width: 10 # 1 means greedy
generate_top_k: 5
unstick_by_beam_search: False # use beam-search for failed actions, set True during evaluation
training:
max_nb_steps_per_episode: 50 # terminate after this many steps
fraction_assist:
fraction_assist_anneal_episodes: 50000
fraction_assist_anneal_from: 1.0
fraction_assist_anneal_to: 0.01
fraction_random:
fraction_random_anneal_episodes: 0
fraction_random_anneal_from: 0.0
fraction_random_anneal_to: 0.0
replay:
replay_memory_capacity: 500000
update_per_k_game_steps: 5
replay_batch_size: 64
replay_sample_history_length: 4
replay_sample_update_from: 2
vision_dagger:
model_type: "resnet" # 'resnet' (whole image features) or 'maskrcnn_whole' (whole image MaskRCNN feats) or 'maskrcnn' (top k MaskRCNN detection feats) or 'no_vision' (zero vision input)
resnet_fc_dim: 64
maskrcnn_top_k_boxes: 10 # top k box features
use_exploration_frame_feats: False # append feats from initial exploration (memory intensive!)
sequence_aggregation_method: "average" # 'sum' or 'average' or 'rnn'